The purpose of this paper is to analyse the effects of predatory approach words in the detection of cyberbullying and to propose a mechanism of generating a dictionary of such approach words. The research incorporates analysis of chat logs from convicted felons, to generate a dictionary of sexual approach words. By analysing data across multiple social networks, the study demonstrates the usefulness of such a dictionary of approach words in detection of online predatory behaviour through machine learning algorithms. It also shows the difference between the nature of contents across specific social network platforms. The proposed solution to detect cyberbullying and the domain of approach words are scalable to fit real-life social media, which can have a positive impact on the overall health of online social networks. Different types of cyberbullying have different characteristics. However, existing cyberbullying detection works are not targeted towards any of these specific types. This research is tailored to focus on sexual harassment type of cyberbullying and proposes a novel dictionary of approach words. Since cyberbullying is a growing threat to the mental health and intellectual development of adolescents in the society, models targeted towards the detection of specific type of online bullying or predation should be encouraged among social network researchers.

中文翻译：

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网络欺凌的骚扰类型检测：逼近词词典及其影响

本文的目的是分析掠夺性接近词在网络欺凌检测中的作用，并提出一种生成此类接近词词典的机制。该研究结合了对被定罪的重罪犯的聊天记录的分析，以生成性方法词汇词典。通过分析跨多个社交网络的数据，该研究证明了这种接近词词典在通过机器学习算法检测在线掠夺行为方面的有用性。它还显示了跨特定社交网络平台的内容性质之间的差异。提议的检测网络欺凌和接近词域的解决方案可扩展以适应现实生活中的社交媒体，这可以对在线社交网络的整体健康产生积极影响。不同类型的网络欺凌具有不同的特征。然而，现有的网络欺凌检测工作并不针对任何这些特定类型。这项研究专门针对性骚扰类型的网络欺凌，并提出了一种新的接近词词典。由于网络欺凌对社会青少年的心理健康和智力发展构成越来越大的威胁，因此社交网络研究人员应该鼓励针对特定类型的网络欺凌或掠夺行为进行检测的模型。